Optimizing Data Pipelines: Staging Models Best Practices and Limiting View Runs

Are you grappling with questions surrounding staging models and materializing views in your dbt projects? In this guide, we delve into best practices and solutions to optimize your data pipelines for efficiency and cost-effectiveness.

Structuring Projects for Success

Central to a well-organized dbt project is a structured approach to modeling. Our recommended project structure encompasses three primary layers:

1. Staging: Build initial modular blocks from source data.
2. Intermediate: Layer logic for data preparation and transformation.
3. Marts: Combine modular pieces into comprehensive organizational entities.

Embracing this structure, especially prioritizing a staging layer predominantly composed of views, can yield significant benefits, including reduced code duplication and storage costs.

Excluding Views in dbt Cloud Jobs

Have you wondered how to exclude unchanged views from your dbt Cloud job runs to optimize performance? Here's a solution:

• Exclude views by modifying your dbt Cloud job command to include: --exclude config.materialized:view.
• Consider the implications carefully, especially if your views contain dynamic code or require testing with each run.

Custom Selectors for Job Commands

To fine-tune your job runs and exclude views while still running tests, follow these steps:

1. Create a custom selector named skip_views_but_test_views.
2. Configure the selector to skip materializing views while ensuring tests for views are still executed.
3. Apply the selector to your dbt Cloud jobs with --selector skip_views_but_test_views.

Building Only Changed Views

For even greater optimization, set up a merge job triggered upon code merges into main:

• Create a merge job in dbt Cloud, specifying commands to run only modified views.
• Use the merge job to deploy changes from PRs to production promptly, optimizing platform spend and job durations.

While setting up and testing these optimizations requires effort, the benefits — including significant reductions in job runtime — justify the investment.

#DataPipelines #dbt #DataOptimization #DataManagement #DataModeling #DataWarehouse #DataAnalytics